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All Journal Statistika Jurnal Peluang Proceedings of Annual International Conference Syiah Kuala University - Social Sciences Chapter Jurnal Gaussian Jurnal Pengajaran MIPA Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Didaktik Matematika Jurnal Pengajaran Matematika dan Ilmu Pengetahuan Alam Jurnal Riset Pendidikan Matematika Jurnal Elemen Eduma : Mathematics Education Learning and Teaching Jurnal Informatika Upgris Jurnal Penelitian Pendidikan IPA (JPPIPA) Numeracy : Jurnal Ilmiah Pendidikan Matematika International Journal for Educational and Vocational Studies JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) ESTIMASI: Journal of Statistics and Its Application Journal of Applied Data Sciences Narra J International Journal of Education and Digital Learning (IJEDL) Jurnal Natural Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) Heca Journal of Applied Sciences Journal of Educational Management and Learning Infolitika Journal of Data Science PESARE: Science and Engineering Service Journal
Muhammad Subianto
Department Of Statistics, Faculty Of Mathematics And Natural Science, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Journal : Journal of Applied Data Sciences

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Hybrid Ensemble Learning with SMOTEENN and Soft Voting for Stunting Risk Prediction: A SHAP-Based Explainable Approach Furqany, Nuwairy El; Subianto, Muhammad; Rusyana, Asep
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.829

Abstract

Stunting remains a critical public health concern in Indonesia, with long-term consequences for physical growth, cognitive development, and human capital. This study introduces a hybrid machine learning framework to predict household-level stunting risk by integrating Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN), soft voting ensemble, and SHapley Additive exPlanations (SHAP). The objective is to enhance both predictive accuracy and interpretability in identifying high-risk households. A dataset of 115,579 household records from West Sumatra, comprising 20 demographic, socioeconomic, health, and housing predictors, was utilized. Preprocessing steps included handling missing values, categorical encoding, and applying SMOTEENN exclusively on the training set to mitigate class imbalance. The baseline models demonstrated limited sensitivity, with XGBoost performing best at 74.56% accuracy and 71.08% F1-score on imbalanced data. After applying SMOTEENN, performance improved substantially, with XGBoost achieving 91.82% accuracy and 91.74% F1-score. Further improvements were obtained through hybridization, where the Random Forest and XGBoost soft voting ensemble reached 91.95% accuracy and 92.46% F1-score, representing a notable gain over individual classifiers. SHAP analysis added interpretability by identifying family members, education level, diverse food consumption, occupation, and drinking water source as dominant predictors of stunting risk. The novelty of this study lies in the integration of SMOTEENN with ensemble learning and SHAP, providing not only robust performance but also transparency in feature contributions. The findings demonstrate that the proposed framework improves sensitivity to minority classes, delivers superior predictive accuracy compared to baseline models, and offers interpretable insights to guide targeted interventions. By combining methodological rigor with explainability, this research contributes a practical decision-support tool for policymakers, supporting early detection of at-risk households and accelerating stunting reduction efforts in Indonesia.
Co-Authors . Zulfan Afidh, Razief Perucha Fauzie Ahmad, Noor Atinah Ainal Mardhiah Akhyar, Fikrul ALFIAN FUTUHUL HADI Almunir Sihotang Asep Rusyana Azzahra, Syarifah Fathimah Bagus Sartono Cut Morina Zubainur Cut Mulyawati Cut Rina Rossalina Dwi Fadhiliani Earlia, Nanda Essy Harnelly EVI RAMADHANI Farsiah, Laina Fitriana AR Furqany, Nuwairy El Ghazi Mauer Idroes Hijriyana P., Meildha Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. INA YATUL ULYA Indah Manfaati Nur Irnanda , Irnanda, Irnanda Irvanizam, Irvanizam Jamil, Muhammad Salsabila Kairupan, Tara S. Kurniadinur, Kurniadinur M. Ikhsan M. Ikhsan M. Ikhsan Maulana, Aga Miftahuddin Miftahuddin Miftahuddin Miftahuddin Miftahuddin Mikyal Bulqiah, Mikyal Misbullah, Alim Muhammad Al Agani Muhammad Iqbal Muhammad Irfan Mukhamad Najib Mursyida, Waliam Nazaruddin Niode, Nurdjannah Jane Nisya Fajri Noviandy, Teuku R. Nurbaiti Nurbaiti Nurdjannah J. Niode Nurjani Nurjani Nurjannah Nurjannah Nurleila, Nurleila Prakoeswa, Cita RS. Purnama Mulia Farib Rahmah Johar Razief Perucha Fauzie Afidh Reza Wafdan Rika Fitriani Rika Siviani Rinaldi Idroes Rizal Munadi RR. Ella Evrita Hestiandari S.Pd. M Kes I Ketut Sudiana . Salmawaty Salmawati Salmawaty Salmawaty Sasmita, Novi Reandy sufriani, sufriani Sugara, Dimas Rendy Suhartono Suhendra, Rivansyah Suryadi Suryadi Teuku Rizky Noviandy Tuti Asmiati Vivi Dina Melani Vivi Dina Melani Vivi Dina Melani Widya Sari Wira Dharma Wisnu Ananta Kusuma Yusrizal Yusrizal Zahriah, Zahriah Zainal Abidin Zainal Abidin Zhilalmuhana, Teuku Zulfan